Assessment of Trametes versicolor, Isochrysis galbana, and Purple Phototrophic Bacteria for the Removal of Pharmaceutical Compounds in Hospital Wastewater
Bibliographic record
Abstract
Hospitals are one of the key contributors of pharmaceutical contaminants of emerging concern to the sewer systems. Hospitals wastewaters contain concentrations of pharmaceutical compounds between 3 and 150, which are higher than urban wastewater streams. However, dedicated treatments of the hospital effluents before discharge to the sewer system are not compulsory. Besides, conventional wastewater treatment plants have not been designed to remove pharmaceutical compounds effectively, and consequently, these micropollutants can reach the aquatic ecosystems. The removal of pharmaceutical compounds in real hospital wastewater was gaged using three different microbial cultures (white rot-fungus <italic>Trametes versicolor</italic>, microalga <italic>Isochrysis galbana</italic>, and a mixed culture of non-sulfur purple phototrophic bacteria). Before and after bioassays of the hospital wastewater, environmental hazard quotients were used to evaluate the biological treatment efficiency. Up to 45 out of the 79 compounds included in the analytical method were noticed in the hospital wastewater, with a predominance of analgesics/anti-inflammatories (acetaminophen, ibuprofen, ketoprofen, and naproxen). It was followed by antibiotics (azithromycin, ciprofloxacin, and ofloxacin, out of which the first two are included in the watch list of substances for monitoring in water in 2020) and anti-hypertensive drugs. <italic>Isochrysis galbana</italic> reached a reduction of 45% of the total concentration of pharmaceuticals, whereas <italic>Trametes versicolor</italic> and mixed culture of purple phototrophic bacteria improved the reductions up to 69% and 76%, respectively. Moreover, potential environmental risk compounds (antibiotics, particularly ciprofloxacin and ofloxacin) were removed by <italic>Trametes </italic>v<italic>ersicolor</italic> in higher extension, obtaining a total hazard quotient reduction higher than the other two cultures. Removal efficiency and environmental risk assessment of remaining PhACs were used to evaluate the performance of the new biological systems for the treatment of emerging pollutants. According to both criteria, <italic>T. versicolor</italic> seems the most capable alternative for removing pharmaceutical compounds in hospital wastewater effluents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".